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POIs there any efficient way to dynamically change the compress_matrix in boost?
 

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  1. POIs there any efficient way to dynamically change the compress_matrix in boost?
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    <p>I am using ublas::Compressed Matrix to work with UMFPACK, a sparse linear solver. Since I am doing a simulation, so every time the linear system is constructed slightly differently that might involve enlarging/shrinking the coefficient matrix and some sparse matrix multiplications. The scale of the linear system is around 25k.</p> <p>Even there is a binding patch for boost to work with UMFPACK, I still need to change the matrix from time to time, sometimes even figuring out the number of non-zero values would be time-consuming(ideally, I have to give the number of non-zero values when I initialize a matrix). Also, I use ublas::range to append columns/rows dynamically.</p> <p>So my question is: is there any efficient way to do this? Right now it's too slow for me. Transposing a matrix with dimension like 15k costs nearly 6s and appending about 12k rows is fast(because I guess it's a row-major matrix), but appending the same number of columns to the matrix can cost up to 20s(i guess for the same reason as above, so even I used a column-major matrix the total time required would be the same).</p> <p>Kinda getting desperate here. Any suggestion is welcome.</p> <p>Cheers.</p>
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    1. COSince I had nearly 30 views but no answers, I think maybe my question is not very clear. So here are some details.
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    2. COSince I am doing simulation, for every time step, I have assemble a linear system and solve it which is basically just AX=B. However, the coefficient matrix A is usually composed by three matrices. A weight matrix, two coefficient matrices for soft constraints and hard constraints respectively that cannot be precomputed. (see next comment)
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    3. COBecause solving the linear system is the result of minimizing a quadratic function in a least square sense, I have to do a matrix-matrix multiplication to make a matrix T and a matrix-vector multiplication to make B for integrating the soft constraint matrix the linear system. Then I have to append the hard constraint matrix to the bottom and the right of T in order to make A. Finally, after A and B done, I can input them into UMFPack.(see next comment)
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